Neuro-physiology and neuro-behavioral based stimulus targeting system

ABSTRACT

An example system includes a processor to determine a first distance between a first peak in a first frequency band of neuro-response data gathered from a subject while exposed to media and a second peak in the first frequency band; determine a second distance between a third peak in the first frequency band and either the second peak in the first frequency band or a fourth peak in the first frequency band; determine a first difference between the first distance and the second distance; generate a first response profile for the subject based on the first difference; and integrate the first response profile with a second response profile associated with a second subject to form an integrated response profile. A selector is to select an advertisement or entertainment for presentation based on the integrated response profile. The processor is to modify the media to present the advertisement or entertainment.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent arises from a continuation of U.S. patent application Ser.No. 13/730,550, which was filed on Dec. 28, 2012, which arises from acontinuation of U.S. patent application Ser. No. 12/122,262, which wasfiled on May 16, 2008, and issued as U.S. Pat. No. 8,392,253, and claimsthe benefit under 35 U.S.C. § 119(e) to U.S. Provisional PatentApplication Ser. No. 60/938,286, which was filed on May 16, 2007. U.S.patent application Ser. No. 13/730,550, U.S. patent application Ser. No.12/122,262, and U.S. Provisional Patent Application Ser. No. 60/938,286are all hereby incorporated herein by reference in their entireties.

FIELD OF THE DISCLOSURE

The present disclosure relates to a neuro-physiology and neuro-behaviorbased stimulus target system.

BACKGROUND

Conventional systems for selectively targeting stimulus materials suchas advertising often rely on general geographic, demographic, orstatistical information. In some instances, conventional systemselectively target stimulus materials using survey based responsecollection. However, these mechanisms for selectively targeting stimulusmaterials are limited.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure may best be understood by reference to the followingdescription taken in conjunction with the accompanying drawings, whichillustrate particular examples.

FIG. 1 illustrates one example of a system for performing stimulustargeting.

FIG. 2 illustrates examples of stimulus attributes that can be includedin a stimulus attributes repository.

FIG. 3 illustrates examples of data models that can be used with thestimulus targeting system.

FIG. 4 illustrates one example of a query that can be used with thestimulus targeting system.

FIG. 5 illustrates one example of a report generated using the stimulustargeting system.

FIG. 6 illustrates one example of a technique for performing stimulustargeting.

FIG. 7 provides one example of a system that can be used to implementone or more mechanisms.

DETAILED DESCRIPTION

Reference will now be made in detail to some specific examples of thedisclosure including the best modes contemplated by the inventors forcarrying out the teachings of the disclosure. Examples of these specificexamples are illustrated in the accompanying drawings. While thedisclosure is described in conjunction with these specific examples, itwill be understood that it is not intended to limit the disclosure tothe described examples. On the contrary, it is intended to coveralternatives, modifications, and equivalents as may be included withinthe spirit and scope of the disclosure as defined by the appendedclaims.

For example, the techniques and mechanisms of the present disclosurewill be described in the context of particular types ofneuro-physiological and neuro-behavioral data. However, it should benoted that the techniques and mechanisms of the present disclosure applyto a variety of different types of data. It should be noted that variousmechanisms and techniques can be applied to any type of stimuli. In thefollowing description, numerous specific details are set forth in orderto provide a thorough understanding of the present disclosure.Particular examples of the present disclosure may be implemented withoutsome or all of these specific details. In other instances, well knownprocess operations have not been described in detail in order not tounnecessarily obscure the present disclosure.

Various techniques and mechanisms of the present disclosure willsometimes be described in singular form for clarity. However, it shouldbe noted that some examples include multiple iterations of a techniqueor multiple instantiations of a mechanism unless noted otherwise. Forexample, a system uses a processor in a variety of contexts. However, itwill be appreciated that a system can use multiple processors whileremaining within the scope of the present disclosure unless otherwisenoted. Furthermore, the techniques and mechanisms of the presentdisclosure will sometimes describe a connection between two entities. Itshould be noted that a connection between two entities does notnecessarily mean a direct, unimpeded connection, as a variety of otherentities may reside between the two entities. For example, a processormay be connected to memory, but it will be appreciated that a variety ofbridges and controllers may reside between the processor and memory.Consequently, a connection does not necessarily mean a direct, unimpededconnection unless otherwise noted.

Overview

Consequently, it is desirable to provide improved methods and apparatusfor providing a stimulus targeting system.

A system performs stimulus targeting using neuro-physiological andneuro-behavioral data. Subjects are exposed to stimulus material such asmarketing and entertainment materials and data is collected usingmechanisms such as Electroencephalography (EEG), Galvanic Skin Response(GSR), Electrocardiograms (EKG), Electrooculography (EOG), eye tracking,and facial emotion encoding. Neuro-physiological and neuro-behavioraldata collected is analyzed to select targeted stimulus materials. Thetargeted stimulus materials are provided to particular subjects for avariety of purposes.

Examples

Conventional stimulus targeting systems typically target generalgeographic areas and demographic groups and do not have the resolutionto target narrow audiences or individuals. Some efforts have been madeto selectively target narrow audiences or individuals, but these effortshave been limited because of a variety of reasons.

For example, subjects are required to complete surveys after initial andsubsequent exposures to stimulus material such as an advertisement. Thesurvey responses are analyzed to determine possible patterns. However,survey results often provide only limited information. For example,survey subjects may be unable or unwilling to express their truethoughts and feelings about a topic, or questions may be phrased withbuilt in bias. Articulate subjects may be given more weight thannon-expressive ones. Analysis of multiple survey responses andcorrelation of the responses to stimulus material is also limited. Avariety of semantic, syntactic, metaphorical, cultural, social andinterpretive biases and errors prevent accurate and repeatableevaluation.

Consequently, the techniques and mechanisms of the present disclosureuse neuro-physiological and neuro-behavioral response measurements suchas central nervous system, autonomic nervous system, and effectormeasurements to improve stimulus targeting. Some examples of centralnervous system measurement mechanisms include Functional MagneticResonance Imaging (fMRI) and Electroencephalography (EEG). fMRI measuresblood oxygenation in the brain that correlates with increased neuralactivity. However, current implementations of fMRI have poor temporalresolution of few seconds. EEG measures electrical activity associatedwith post synaptic currents occurring in the milliseconds range.Subcranial EEG can measure electrical activity with the most accuracy,as the bone and dermal layers weaken transmission of a wide range offrequencies. Nonetheless, surface EEG provides a wealth ofelectrophysiological information if analyzed properly.

Autonomic nervous system measurement mechanisms include Galvanic SkinResponse (GSR), Electrocardiograms (EKG), pupillary dilation, etc.Effector measurement mechanisms include Electrooculography (EOG), eyetracking, facial emotion encoding, reaction time etc.

According to various examples, the techniques and mechanisms of thepresent disclosure intelligently blend multiple modes and manifestationsof precognitive neural signatures with cognitive neural signatures andpost cognitive neurophysiological manifestations to more accuratelyallow selective targeting of stimulus materials. In some examples,autonomic nervous system measures are themselves used to validatecentral nervous system measures. Effector and behavior responses areblended and combined with other measures. According to various examples,central nervous system, autonomic nervous system, and effector systemmeasurements are aggregated into a measurement that allows selectivetargeting of stimulus materials.

In particular examples, multiple subjects are exposed to stimulusmaterial and data such as neuro-physiological and neuro-behavioral data.According to various examples, the multiple subjects may be exposedsimultaneously to stimulus material in a large group setting, inmultiple small group settings, in relatively isolated settings, etc. Themultiple subjects may or may not be allowed to interact directly orindirectly. Response data collected during exposure of the multiplesubjects is analyzed and integrated to determine neuro-physiological andneuro-behavioral response data. According to various examples, responsedata is analyzed and enhanced for each subject and further analyzed andenhanced by integrating data across multiple subjects to select stimulusmaterials to provide to particular subjects.

According to various examples, neuro-physiological and neuro-behavioraldata may show particular effectiveness of stimulus material for aparticular subset of individuals. In particular examples,neuro-physiological and neuro-behavioral data may show profiles ofresponses for particular subjects based on attributes of the stimulusmaterial. Targeted stimulus materials may be intelligently selectedusing neuro-physiological and neuro-behavioral data and known attributesof the stimulus materials. In some examples, survey results and focusgroup information can also be used to elicit further insights onselecting stimulus materials. The additional stimulus materials selectedmay be used to obtain additional neuro-physiological andneuro-behavioral information from particular subjects. The additionalstimulus materials may also be selected as materials that would beparticularly effective in an advertising campaign or mailing campaign.Stimulus materials may be targeted to narrow audiences, individuals, oreven specific subgroups or larger populations.

A variety of stimulus materials such as entertainment and marketingmaterials, media streams, billboards, print advertisements, textstreams, music, performances, sensory experiences, etc. can be analyzed.According to various examples, enhanced neuro-physiological andneuro-behavioral data is generated using a data analyzer that performsboth intra-modality measurement enhancements and cross-modalitymeasurement enhancements. According to various examples, brain activityis measured not just to determine the regions of activity, but todetermine interactions and types of interactions between variousregions. The techniques and mechanisms of the present disclosurerecognize that interactions between neural regions support orchestratedand organized behavior. Attention, emotion, memory, and other abilitiesare not merely based on one part of the brain but instead rely onnetwork interactions between brain regions.

The techniques and mechanisms of the present disclosure furtherrecognize that different frequency bands used for multi-regionalcommunication can be indicative of the effectiveness of stimuli. Inparticular examples, evaluations are calibrated to each subject andsynchronized across subjects. In particular examples, templates arecreated for subjects to create a baseline for measuring pre and poststimulus differentials. According to various examples, stimulusgenerators are intelligent and adaptively modify specific parameterssuch as exposure length and duration for each subject being analyzed. Anintelligent stimulus generation mechanism intelligently adapts outputfor particular users and purposes. A variety of modalities can be usedincluding EEG, GSR, EKG, pupillary dilation, EOG, eye tracking, facialemotion encoding, reaction time, etc. Individual modalities such as EEGare enhanced by intelligently recognizing neural region communicationpathways.

Cross modality analysis is enhanced using a synthesis and analyticalblending of central nervous system, autonomic nervous system, andeffector signatures. Synthesis and analysis by mechanisms such as timeand phase shifting, correlating, and validating intra-modaldeterminations allow generation of a composite output characterizing thesignificance of various data responses. Responses can be integratedacross subjects and additional stimulus material can be targeted toparticular subjects and groups using responses and stimulus materialattributes.

FIG. 1 illustrates one example of a system for performing stimulustargeting using central nervous system, autonomic nervous system, andeffector measures. According to various examples, the stimulus targetingsystem includes a protocol generator and presenter device 101. Inparticular examples, the protocol generator and presenter device 101 ismerely a presenter device and merely presents stimulus material to auser. The stimulus material may be a media clip, a commercial, pages oftext, a brand image, a performance, a magazine advertisement, a movie,an audio presentation, particular tastes, smells, textures and/orsounds. The stimuli can involve a variety of senses and occur with orwithout human supervision. Continuous and discrete modes are supported.According to various examples, the protocol generator and presenterdevice 101 also has protocol generation capability to allow intelligentcustomization of stimuli provided to multiple subjects.

According to various examples, the subjects are connected to datacollection devices 105. The data collection devices 105 may include avariety of neuro-physiological and neuro-behavioral measurementmechanisms such as EEG, EOG, GSR, EKG, pupillary dilation, eye tracking,facial emotion encoding, and reaction time devices, etc. According tovarious examples, neuro-physiological and neuro-behavioral data includescentral nervous system, autonomic nervous system, and effector data. Inparticular examples, the data collection devices 105 include EEG 111,EOG 113, and GSR 115. In some instances, only a single data collectiondevice is used. Data collection may proceed with or without humansupervision.

The data collection device 105 collects neuro-physiological andneuro-behavioral data from multiple sources. This includes a combinationof devices such as central nervous system sources (EEG), autonomicnervous system sources (GSR, EKG, pupillary dilation), and effectorsources (EOG, eye tracking, facial emotion encoding, reaction time). Inparticular examples, data collected is digitally sampled and stored forlater analysis. In particular examples, the data collected could beanalyzed in real-time. According to particular examples, the digitalsampling rates are adaptively chosen based on the neurophysiological andneurological data being measured.

In one particular example, the stimulus targeting system includes EEG111 measurements made using scalp level electrodes, EOG 113 measurementsmade using shielded electrodes to track eye data, GSR 115 measurementsperformed using a differential measurement system, a facial muscularmeasurement through shielded electrodes placed at specific locations onthe face, and a facial affect graphic and video analyzer adaptivelyderived for each individual.

In particular examples, the data collection devices are clocksynchronized with a protocol generator and presenter device 101. Thedata collection system 105 can collect data from individual subjects (1system), or can be modified to collect synchronized data from multiplesubjects (N+1 system). The N+1 system may include multiple individualssynchronously tested in isolation or in a group setting. In particularexamples, the subjects are placed in a large group setting and areallowed to interact while being exposed to the stimulus material. Inother examples, subjects are placed in a group setting but are allowedonly non-verbal interaction. In still other examples, subjects are notallowed to interact during exposure to stimulus materials. A variety ofarrangements are possible. In particular examples, the data collectiondevices also include a condition evaluation subsystem that provides autotriggers, alerts and status monitoring and visualization components thatcontinuously monitor the status of the subject, data being collected,and the data collection instruments. The condition evaluation subsystemmay also present visual alerts and automatically trigger remedialactions.

According to various examples, the stimulus targeting system alsoincludes a data cleanser device 121. In particular examples, the datacleanser device 121 filters the collected data to remove noise,artifacts, and other irrelevant data using fixed and adaptive filtering,weighted averaging, advanced component extraction (like PCA, ICA),vector and component separation methods, etc. This device cleanses thedata by removing both exogenous noise (where the source is outside thephysiology of the subject) and endogenous artifacts (where the sourcecould be neurophysiological like muscle movement, eye blinks, etc.).

The artifact removal subsystem includes mechanisms to selectivelyisolate and review the response data and identify epochs with timedomain and/or frequency domain attributes that correspond to artifactssuch as line frequency, eye blinks, and muscle movements. The artifactremoval subsystem then cleanses the artifacts by either omitting theseepochs, or by replacing these epoch data with an estimate based on theother clean data (for example, an EEG nearest neighbor weightedaveraging approach).

According to various examples, the data cleanser device 121 isimplemented using hardware, firmware, and/or software. It should benoted that although a data cleanser device 121 is shown located after adata collection device 105 and before data analyzer 181, the datacleanser device 121 like other components may have a location andfunctionality that varies based on system implementation. For example,some systems may not use any automated data cleanser device whatsoeverwhile in other systems, data cleanser devices may be integrated intoindividual data collection devices.

A stimulus attributes repository 131 provides information on thestimulus material being presented to the multiple subjects. According tovarious examples, stimulus attributes include properties of the stimulusmaterials as well as purposes, presentation attributes, reportgeneration attributes, etc. In particular examples, stimulus attributesinclude time span, channel, rating, media, type, etc. Purpose attributesinclude aspiration and objects of the stimulus including excitement,memory retention, associations, etc. Presentation attributes includeaudio, video, imagery, and message needed for enhancement or avoidance.Other attributes may or may not also be included in the stimulusattributes repository or some other repository.

The data cleanser device 121 and the stimulus attributes repository 131pass data to the data analyzer 181. The data analyzer 181 uses a varietyof mechanisms to analyze underlying data in the system to determineneuro-physiological and neuro-behavioral characteristics of stimulusmaterial. According to various examples, the data analyzer customizesand extracts the independent neurological and neuro-physiologicalparameters for each individual in each modality, and blends theestimates within a modality as well as across modalities to elicit anenhanced response to the presented stimulus material. In particularexamples, the data analyzer 181 aggregates the response measures acrosssubjects in a dataset.

According to various examples, neuro-physiological and neuro-behavioralsignatures are measured using time domain analyses and frequency domainanalyses. Such analyses use parameters that are common acrossindividuals as well as parameters that are unique to each individual.The analyses could also include statistical parameter extraction andfuzzy logic based attribute estimation from both the time and frequencycomponents of the synthesized response.

In some examples, statistical parameters used in a blended effectivenessestimate include evaluations of skew, peaks, first and second moments,population distribution, as well as fuzzy estimates of attention,emotional engagement and memory retention responses.

According to various examples, the data analyzer 181 may include anintra-modality response synthesizer and a cross-modality responsesynthesizer. In particular examples, the intra-modality responsesynthesizer is configured to customize and extract the independentneuro-physiological and neuro-behavioral parameters for each individualin each modality and blend the estimates within a modality analyticallyto elicit an enhanced response to the presented stimuli. In particularexamples, the intra-modality response synthesizer also aggregates datafrom different subjects in a dataset.

According to various examples, the cross-modality response synthesizeror fusion device blends different intra-modality responses, includingraw signals and signals output. The combination of signals enhances themeasures of effectiveness within a modality. The cross-modality responsefusion device can also aggregate data from different subjects in adataset.

According to various examples, the data analyzer 181 also includes acomposite enhanced effectiveness estimator (CEEE) that combines theenhanced responses and estimates from each modality to provide a blendedestimate of the effectiveness. In particular examples, blended estimatesare provided for each exposure of a subject to stimulus materials. Theblended estimates are evaluated over time to determineneuro-physiological and neuro-behavioral characteristics. According tovarious examples, numerical values are assigned to each blendedestimate. The numerical values may correspond to the intensity ofneuro-physiological and neuro-behavioral measurements, the significanceof peaks, the change between peaks, etc. Higher numerical values maycorrespond to higher significance in neuro-physiological andneuro-behavioral intensity. Lower numerical values may correspond tolower significance or even insignificance neuro-physiological andneuro-behavioral activity. In other examples, multiple values areassigned to each blended estimate. In still other examples, blendedestimates of neuro-physiological and neuro-behavioral significance aregraphically represented to show effectiveness for different individualsor groups.

According to various examples, the data analyzer 181 provides analyzedand enhanced response data to a data communication system 183. Accordingto various examples, the data communication system 183 provides rawand/or analyzed data and insights to the response integration system. Inparticular examples, the data communication system 183 may includemechanisms for the compression and encryption of data for secure storageand communication.

According to various examples, the data communication system 183transmits data to the response integration using protocols such as theFile Transfer Protocol (FTP), Hypertext Transfer Protocol (HTTP) alongwith a variety of conventional, bus, wired network, wireless network,satellite, and proprietary communication protocols. The data transmittedcan include the data in its entirety, excerpts of data, converted data,and/or elicited response measures.

In particular examples, the data communication system 183 sends data toresponse integration system 185. According to various examples, theresponse integration system 185 combines analyzed and enhanced responsesto the stimulus material while using information about stimulus materialattributes. In particular examples, the response integration system 185also collects and integrates user behavioral and survey responses withthe analyzed and enhanced response data to more effectively determineneuro-physiological and neuro-behavioral response to stimulus materials.

According to various examples, the response integration system 185obtains attributes such as requirements and purposes of the stimulusmaterial presented. Some of these requirements and purposes may beobtained from a stimulus attribute repository 131. Others may beobtained from other sources. In particular examples, the requirementscollected include attributes of the stimulus material including channel,media, time span, audience, demographic target. Other purposes mayinvolve the target objectives of the stimulus material, such as memoryretention of a brand name, association of a product with a particularfeeling, etc. Still other attributes may include views and presentationspecific attributes such as audio, video, imagery and messages needed,media for enhanced, media for avoidance, etc.

According to various examples, the response integration system 185 alsoincludes mechanisms for the collection and storage of demographic,statistical and/or survey based responses to different entertainment,marketing, advertising and other audio/visual/tactile/olfactorymaterial. If this information is stored externally, the responseintegration system 185 can include a mechanism for the push and/or pullintegration of the data, such as querying, extraction, recording,modification, and/or updating.

According to various examples, the response integration system 185integrates the requirements for the presented material, the assessedneuro-physiological and neuro-behavioral response measures, and theadditional stimulus attributes such as demographic/statistical/surveybased responses into a synthesized measure for the neuro-physiologicaland neuro-behavioral response to the stimuli for the selection oftargeted stimulus material for presentation to particular individuals orgroups.

The response integration system 185 can further include an adaptivelearning component that refines user or group profiles and tracksvariations in the neuro-physiological and neuro-behavioral response toparticular stimuli or series of stimuli over time. This information canbe made available for other purposes, such as use of the information forpresentation attribute decision making. According to various examples,the response integration system 185 integrates analyzed responses tostimuli and uses stimuli attributes to generate information for theselection of selectively targeted additional stimulus material.

As with a variety of the components in the stimulus targeting system,the response integration system 185 and the presentation modificationsystem 187 can be co-located with the rest of the system and the user,or could be implemented in a remote location. It could also beoptionally separated into an assessment repository system that could becentralized or distributed at the provider or providers of the stimulusmaterial. In other examples, the response integration system is housedat the facilities of a third party service provider accessible bystimulus material providers and/or users.

According to various examples, the presentation modification system 187also includes mechanisms for modification of the presentation ofstimulus materials in a manner appropriate for different presentationdevices. For example, the presentation modification system 187 caninclude an automated channel selection device that can be controlledbased on the outputs of the response integration system. In otherexamples, the presentation modification system 187 can includesoftware/hardware calls into an electronic game or gaming consoles formodifying levels and choices in the game. In still other examples, thepresentation modification system 187 can modify the actual images andmessages displayed in entertainment materials based on data from theresponse integration system 185.

In particular examples, the presentation modification system 187modifies portions of a video stream such as a billboard displayed inimages in the video stream in order to customize messages or imagesshown on the billboard. A billboard in a video stream may default to aparticular advertisement but may be modified to target a particularsubject or group of subjects. Messages, audio sequences, and/or anyother type of stimulus material may be modified or adjusted toselectively target stimulus material. In other examples, a product in animage can be dynamically modified to show different brand names based onneuro-physiological and neuro-behavioral responses.

FIG. 2 illustrates examples of data models that may be provided with astimulus attributes repository. According to various examples, astimulus attributes data model 201 includes a channel 203, media type205, time span 207, audience 209, and demographic information 211. Astimulus purpose data model 215 may include intents 217 and objectives219.

According to various examples, intents and objectives may include memoryretention of a brand name, association of a product with a particularfeeling, excitement level for a particular service, etc. The attributesmay be useful in providing targeted stimulus materials to multiplesubjects and tracking and evaluating the effectiveness of the stimulusmaterials.

FIG. 3 illustrates examples of data models that can be used for storageof information associated with tracking and measurement ofneuro-physiological and neuro-behavioral response. According to variousexamples, a dataset data model 301 includes an experiment name 303and/or identifier, client attributes 305, a subject pool 307, logisticsinformation 309 such as the location, date, and time of testing, andstimulus material 311 including stimulus material attributes.

In particular examples, a subject attribute data model 315 includes asubject name 317 and/or identifier, contact information 321, anddemographic attributes 319 that may be useful for review of neurologicaland neuro-physiological data. Some examples of pertinent demographicattributes include marriage status, employment status, occupation,household income, household size and composition, ethnicity, geographiclocation, sex, race. Other fields that may be included in data model 315include shopping preferences, entertainment preferences, and financialpreferences. Shopping preferences include favorite stores, shoppingfrequency, categories shopped, favorite brands. Entertainmentpreferences include network/cable/satellite access capabilities,favorite shows, favorite genres, and favorite actors. Financialpreferences include favorite insurance companies, preferred investmentpractices, banking preferences, and favorite online financialinstruments. A variety of subject attributes may be included in asubject attributes data model 315 and data models may be preset orcustom generated to suit particular purposes.

According to various examples, data models for neuro-feedbackassociation 325 identify experimental protocols 327, modalities included329 such as EEG, EOG, GSR, surveys conducted, and experiment designparameters 333 such as segments and segment attributes. Other fields mayinclude experiment presentation scripts, segment length, segment detailslike stimulus material used, inter-subject variations, intra-subjectvariations, instructions, presentation order, survey questions used,etc. Other data models may include a data collection data model 337.According to various examples, the data collection data model 337includes recording attributes 339 such as station and locationidentifiers, the data and time of recording, and operator details. Inparticular examples, equipment attributes 341 include an amplifieridentifier and a sensor identifier.

Modalities recorded 343 may include modality specific attributes likeEEG cap layout, active channels, sampling frequency, and filters used.EOG specific attributes include the number and type of sensors used,location of sensors applied, etc. Eye tracking specific attributesinclude the type of tracker used, data recording frequency, data beingrecorded, recording format, etc. According to various examples, datastorage attributes 345 include file storage conventions (format, namingconvention, dating convention), storage location, archival attributes,expiry attributes, etc.

A preset query data model 349 includes a query name 351 and/oridentifier, an accessed data collection 353 such as data segmentsinvolved (models, databases/cubes, tables, etc.), access securityattributes 355 included who has what type of access, and refreshattributes 357 such as the expiry of the query, refresh frequency, etc.Other fields such as push-pull preferences can also be included toidentify an auto push reporting driver or a user driven report retrievalsystem.

FIG. 4 illustrates examples of queries that can be performed to obtaindata associated with stimulus targeting. According to various examples,queries are defined from general or customized scripting languages andconstructs, visual mechanisms, a library of preset queries, diagnosticquerying including drill-down diagnostics, and eliciting what ifscenarios. According to various examples, subject attributes queries 415may be configured to obtain data from a neuro-informatics repositoryusing a location 417 or geographic information, session information 421such as testing times and dates, and demographic attributes 419.Demographics attributes include household income, household size andstatus, education level, age of kids, etc.

Other queries may retrieve stimulus material based on shoppingpreferences of subject participants, countenance, physiologicalassessment, completion status. For example, a user may query for dataassociated with product categories, products shopped, shops frequented,subject eye correction status, color blindness, subject state, signalstrength of measured responses, alpha frequency band ringers, musclemovement assessments, segments completed, etc. Experimental design basedqueries may obtain data from a neuro-informatics repository based onexperiment protocols 427, product category 429, surveys included 431,and stimulus provided 433. Other fields that may used include the numberof protocol repetitions used, combination of protocols used, and usageconfiguration of surveys.

Client and industry based queries may obtain data based on the types ofindustries included in testing, specific categories tested, clientcompanies involved, and brands being tested. Response assessment basedqueries 437 may include attention scores 439, emotion scores, 441,retention scores 443, and effectiveness scores 445. Such queries mayobtain materials that elicited particular scores.

Response measure profile based queries may use mean measure thresholds,variance measures, number of peaks detected, etc. Group response queriesmay include group statistics like mean, variance, kurtosis, p-value,etc., group size, and outlier assessment measures. Still other queriesmay involve testing attributes like test location, time period, testrepetition count, test station, and test operator fields. A variety oftypes and combinations of types of queries can be used to efficientlyextract data.

FIG. 5 illustrates examples of reports that can be generated. Accordingto various examples, client assessment summary reports 501 includeeffectiveness measures 503, component assessment measures 505, andneuro-physiological and neuro-behavioral measures 507. Effectivenessassessment measures include composite assessment measure(s),industry/category/client specific placement (percentile, ranking, . . .), actionable grouping assessment such as removing material, modifyingsegments, or fine tuning specific elements, etc, and the evolution ofthe effectiveness profile over time. In particular examples, componentassessment reports include component assessment measures like attention,emotional engagement scores, percentile placement, ranking, etc.Component profile measures include time based evolution of the componentmeasures and profile statistical assessments. According to variousexamples, reports include the number of times material is assessed,attributes of the multiple presentations used, evolution of the responseassessment measures over the multiple presentations, and usagerecommendations.

According to various examples, client cumulative reports 511 includemedia grouped reporting 513 of all stimulus assessed, campaign groupedreporting 515 of stimulus assessed, and time/location grouped reporting517 of stimulus assessed. According to various examples, industrycumulative and syndicated reports 521 include aggregate assessmentresponses measures 523, top performer lists 525, bottom performer lists527, outliers 529, and trend reporting 531. In particular examples,tracking and reporting includes specific products, categories,companies, brands.

FIG. 6 illustrates one example of stimulus targeting. At 601, a protocolis generated and stimulus material is provided to one or more subjects.According to various examples, stimulus includes streaming video, mediaclips, printed materials, presentations, performances, games, etc. Theprotocol determines the parameters surrounding the presentation ofstimulus, such as the number of times shown, the duration of theexposure, sequence of exposure, segments of the stimulus to be shown,etc. Subjects may be isolated during exposure or may be presentedmaterials in a group environment with or without supervision. At 603,subject responses are collected using a variety of modalities, such asEEG, ERP, EOG, GSR, etc. In some examples, verbal and written responsescan also be collected and correlated with neurological andneurophysiological responses. At 605, data is passed through a datacleanser to remove noise and artifacts that may make data more difficultto interpret. According to various examples, the data cleanser removesEEG electrical activity associated with blinking and otherendogenous/exogenous artifacts.

At 609, data analysis is performed. Data analysis may includeintra-modality response synthesis and cross-modality response synthesisto enhance effectiveness measures. It should be noted that in someparticular instances, one type of synthesis may be performed withoutperforming other types of synthesis. For example, cross-modalityresponse synthesis may be performed with or without intra-modalitysynthesis.

A variety of mechanisms can be used to perform data analysis 609. Inparticular examples, a stimulus attributes repository 131 is accessed toobtain attributes and characteristics of the stimulus materials, alongwith purposes, intents, objectives, etc. In particular examples, EEGresponse data is synthesized to provide an enhanced assessment ofeffectiveness. According to various examples, EEG measures electricalactivity resulting from thousands of simultaneous neural processesassociated with different portions of the brain. EEG data can beclassified in various bands. According to various examples, brainwavefrequencies include delta, theta, alpha, beta, and gamma frequencyranges. Delta waves are classified as those less than 4 Hz and areprominent during deep sleep. Theta waves have frequencies between 3.5 to7.5 Hz and are associated with memories, attention, emotions, andsensations. Theta waves are typically prominent during states ofinternal focus.

Alpha frequencies reside between 7.5 and 13 Hz and typically peak around10 Hz. Alpha waves are prominent during states of relaxation. Beta waveshave a frequency range between 14 and 30 Hz. Beta waves are prominentduring states of motor control, long range synchronization between brainareas, analytical problem solving, judgment, and decision making. Gammawaves occur between 30 and 60 Hz and are involved in binding ofdifferent populations of neurons together into a network for the purposeof carrying out a certain cognitive or motor function, as well as inattention and memory. Because the skull and dermal layers attenuatewaves in this frequency range, brain waves above 75-80 Hz are difficultto detect and are often not used for stimuli response assessment.

However, the techniques and mechanisms of the present disclosurerecognize that analyzing high gamma band (kappa-band: Above 60 Hz)measurements, in addition to theta, alpha, beta, and low gamma bandmeasurements, enhances neurological attention, emotional engagement andretention component estimates. In particular examples, EEG measurementsincluding difficult to detect high gamma or kappa band measurements areobtained, enhanced, and evaluated. Subject and task specific signaturesub-bands in the theta, alpha, beta, gamma and kappa bands areidentified to provide enhanced response estimates. According to variousexamples, high gamma waves (kappa-band) above 80 Hz (typicallydetectable with sub-cranial EEG and/or magnetoencephalography) can beused in inverse model-based enhancement of the frequency responses tothe stimuli.

Various examples of the present disclosure recognize that particularsub-bands within each frequency range have particular prominence duringcertain activities. A subset of the frequencies in a particular band isreferred to herein as a sub-band. For example, a sub-band may includethe 40-45 Hz range within the gamma band. In particular examples,multiple sub-bands within the different bands are selected whileremaining frequencies are band pass filtered. In particular examples,multiple sub-band responses may be enhanced, while the remainingfrequency responses may be attenuated.

An information theory based band-weighting model is used for adaptiveextraction of selective dataset specific, subject specific, taskspecific bands to enhance the effectiveness measure. Adaptive extractionmay be performed using fuzzy scaling. Stimuli can be presented andenhanced measurements determined multiple times to determine thevariation profiles across multiple presentations. Determining variousprofiles provides an enhanced assessment of the primary responses aswell as the longevity (wear-out) of the marketing and entertainmentstimuli. The synchronous response of multiple individuals to stimulipresented in concert is measured to determine an enhanced across subjectsynchrony measure of effectiveness. According to various examples, thesynchronous response may be determined for multiple subjects residing inseparate locations or for multiple subjects residing in the samelocation.

Although a variety of synthesis mechanisms are described, it should berecognized that any number of mechanisms can be applied—in sequence orin parallel with or without interaction between the mechanisms.

Although intra-modality synthesis mechanisms provide enhancedsignificance data, additional cross-modality synthesis mechanisms canalso be applied. A variety of mechanisms such as EEG, Eye Tracking, GSR,EOG, and facial emotion encoding are connected to a cross-modalitysynthesis mechanism. Other mechanisms as well as variations andenhancements on existing mechanisms may also be included. According tovarious examples, data from a specific modality can be enhanced usingdata from one or more other modalities. In particular examples, EEGtypically makes frequency measurements in different bands like alpha,beta and gamma to provide estimates of significance. However, thetechniques of the present disclosure recognize that significancemeasures can be enhanced further using information from othermodalities.

For example, facial emotion encoding measures can be used to enhance thevalence of the EEG emotional engagement measure. EOG and eye trackingsaccadic measures of object entities can be used to enhance the EEGestimates of significance including but not limited to attention,emotional engagement, and memory retention. According to variousexamples, a cross-modality synthesis mechanism performs time and phaseshifting of data to allow data from different modalities to align. Insome examples, it is recognized that an EEG response will often occurhundreds of milliseconds before a facial emotion measurement changes.Correlations can be drawn and time and phase shifts made on anindividual as well as a group basis. In other examples, saccadic eyemovements may be determined as occurring before and after particular EEGresponses. According to various examples, time corrected GSR measuresare used to scale and enhance the EEG estimates of significanceincluding attention, emotional engagement and memory retention measures.

Evidence of the occurrence or non-occurrence of specific time domaindifference event-related potential components (like the DERP) inspecific regions correlates with subject responsiveness to specificstimulus. According to various examples, ERP measures are enhanced usingEEG time-frequency measures (ERPSP) in response to the presentation ofthe marketing and entertainment stimuli. Specific portions are extractedand isolated to identify ERP, DERP and ERPSP analyses to perform. Inparticular examples, an EEG frequency estimation of attention, emotionand memory retention (ERPSP) is used as a co-factor in enhancing theERP, DERP and time-domain response analysis.

EOG measures saccades to determine the presence of attention to specificobjects of stimulus. Eye tracking measures the subject's gaze path,location and dwell on specific objects of stimulus. According to variousexamples, EOG and eye tracking is enhanced by measuring the presence oflambda waves (a neurophysiological index of saccade effectiveness) inthe ongoing EEG in the occipital and extra striate regions, triggered bythe slope of saccade-onset to estimate the significance of the EOG andeye tracking measures. In particular examples, specific EEG signaturesof activity such as slow potential shifts and measures of coherence intime-frequency responses at the Frontal Eye Field (FEF) regions thatpreceded saccade-onset are measured to enhance the effectiveness of thesaccadic activity data.

GSR typically measures the change in general arousal in response tostimulus presented. According to various examples, GSR is enhanced bycorrelating EEG/ERP responses and the GSR measurement to get an enhancedestimate of subject engagement. The GSR latency baselines are used inconstructing a time-corrected GSR response to the stimulus. Thetime-corrected GSR response is co-factored with the EEG measures toenhance GSR significance measures.

According to various examples, facial emotion encoding uses templatesgenerated by measuring facial muscle positions and movements ofindividuals expressing various emotions prior to the testing session.These individual specific facial emotion encoding templates are matchedwith the individual responses to identify subject emotional response. Inparticular examples, these facial emotion encoding measurements areenhanced by evaluating inter-hemispherical asymmetries in EEG responsesin specific frequency bands and measuring frequency band interactions.The techniques of the present disclosure recognize that not only areparticular frequency bands significant in EEG responses, but particularfrequency bands used for communication between particular areas of thebrain are significant. Consequently, these EEG responses enhance theEMG, graphic and video based facial emotion identification.

At 611, processed data is provided to a data communication device.Integrated responses are generated at 613. According to variousexamples, the data communication system data to the response integrationusing protocols such as the File Transfer Protocol (FTP), HypertextTransfer Protocol (HTTP) along with a variety of conventional, bus,wired network, wireless network, satellite, and proprietarycommunication protocols. The data transmitted can include the data inits entirety, excerpts of data, converted data, and/or elicited responsemeasures.

In particular examples, the data communication system sends data to theresponse integration system. According to various examples, the responseintegration system combines analyzed and enhanced responses to thestimulus material while using information about stimulus materialattributes. In particular examples, the response integration system alsocollects and integrates user behavioral and survey responses with theanalyzed and enhanced response data to more effectively measure andtrack neuro-physiological and neuro-behavioral response to stimulusmaterials. According to various examples, the response integrationsystem obtains attributes such as requirements and purposes of thestimulus material presented.

Some of these requirements and purposes may be obtained from a varietyof databases. According to various examples, the response integrationsystem also includes mechanisms for the collection and storage ofdemographic, statistical and/or survey based responses to differententertainment, marketing, advertising and otheraudio/visual/tactile/olfactory material. If this information is storedexternally, the response integration system can include a mechanism forthe push and/or pull integration of the data, such as querying,extraction, recording, modification, and/or updating.

The response integration system can further include an adaptive learningcomponent that refines user or group profiles and tracks variations inthe neuro-physiological and neuro-behavioral response to particularstimuli or series of stimuli over time. This information can be madeavailable for other purposes, such as use of the information forpresentation attribute decision making. According to various examples,the response integration system builds and uses responses of usershaving similar profiles and demographics to provide integrated responsesat 613. At 615, presentation of stimulus is modified to allow selectivetargeting of stimulus materials. According to various examples,additional stimulus materials for presentation to a particular subjector group of subjects are automatically selected based on integratedresponses. In particular examples, a channel is automatically changed.

According to various examples, the targeted stimulus material isselected to elicit an as effective or more effective integrated responsefrom the subject than the stimulus material originally presented to thesubject. According to various examples, additional stimulus material isselected based on attribute similarities with the original stimulusmaterial. In particular examples, various neurological andneurophysiological measurements and combinations including attention,emotion, and memory retention are used to determine the significance ofneuro-physiological and neuro-behavioral responses.

According to various examples, various mechanisms such as the datacollection mechanisms, the intra-modality synthesis mechanisms,cross-modality synthesis mechanisms, etc. are implemented on multipledevices. However, it is also possible that the various mechanisms beimplemented in hardware, firmware, and/or software in a single system.FIG. 7 provides one example of a system that can be used to implementone or more mechanisms. For example, the system shown in FIG. 7 may beused to implement a data analyzer.

According to particular examples, a system 700 suitable for implementingparticular examples of the present disclosure includes a processor 701,a memory 703, an interface 711, and a bus 715 (e.g., a PCI bus). Whenacting under the control of appropriate software or firmware, theprocessor 701 is responsible for such tasks such as pattern generation.Various specially configured devices can also be used in place of aprocessor 701 or in addition to processor 701. The completeimplementation can also be done in custom hardware. The interface 711 istypically configured to send and receive data packets or data segmentsover a network. Particular examples of interfaces the device supportsinclude host bus adapter (HBA) interfaces, Ethernet interfaces, framerelay interfaces, cable interfaces, DSL interfaces, token ringinterfaces, and the like.

In addition, various very high-speed interfaces may be provided such asfast Ethernet interfaces, Gigabit Ethernet interfaces, ATM interfaces,HSSI interfaces, POS interfaces, FDDI interfaces and the like.Generally, these interfaces may include ports appropriate forcommunication with the appropriate media. In some cases, they may alsoinclude an independent processor and, in some instances, volatile RAM.The independent processors may control such communications intensivetasks as data synthesis.

According to particular examples, the system 700 uses memory 703 tostore data, algorithms and program instructions. The programinstructions may control the operation of an operating system and/or oneor more applications, for example. The memory or memories may also beconfigured to store received data and process received data.

Because such information and program instructions may be employed toimplement the systems/methods described herein, the present disclosurerelates to tangible, machine readable media that include programinstructions, state information, etc. for performing various operationsdescribed herein. Examples of machine-readable media include, but arenot limited to, magnetic media such as hard disks, floppy disks, andmagnetic tape; optical media such as CD-ROM disks and DVDs;magneto-optical media such as optical disks; and hardware devices thatare specially configured to store and perform program instructions, suchas read-only memory devices (ROM) and random access memory (RAM).Examples of program instructions include both machine code, such asproduced by a compiler, and files containing higher level code that maybe executed by the computer using an interpreter.

Although the foregoing disclosure has been described in some detail forpurposes of clarity of understanding, it will be apparent that certainchanges and modifications may be practiced within the scope of theappended claims. Therefore, the present examples are to be considered asillustrative and not restrictive and the disclosure is not to be limitedto the details given herein, but may be modified within the scope andequivalents of the appended claims.

1. A system for transforming a media presentation based onneuro-response data, the system comprising: a processor to: determine afirst distance between (1) a first peak in a first frequency band offirst neuro-response data gathered from a first subject while exposed tomedia and (2) a second peak in the first frequency band; determine asecond distance between (1) a third peak in the first frequency band andeither (2) the second peak in the first frequency band or (3) a fourthpeak in the first frequency band; determine a first difference betweenthe first distance and the second distance; generate a first responseprofile for the first subject to the media based on the firstdifference; and integrate the first response profile with a secondresponse profile associated with a second subject exposed to the mediato form an integrated response profile for the media; and a selector toselect a first advertisement or entertainment for presentation based onthe integrated response profile, the processor to modify the media topresent the first advertisement or entertainment.
 2. The system of claim1, wherein the first response profile and the second response profileshare a characteristic.
 3. The system of claim 1, wherein thecharacteristic is indicative of a response of the first subject to themedia and a response of the second subject to the media.
 4. The systemof claim 1, wherein the first subject and the second subject share ademographic attribute.
 5. The system of claim 1, wherein the firstdifference indicates a level of engagement.
 6. The system of claim 1,wherein the processor is to: analyze second neuro-response data gatheredfrom the first subject while exposed to the first advertisement orentertainment; update the first response profile based on the analysisof the second neuro-response data; and update the integrated responseprofile based on the updated first response profile to form an updatedintegrated response profile.
 7. The system of claim 6, wherein theprocessor is to select a second advertisement based on the integratedresponse profile or the updated integrated response profile.
 8. Thesystem of claim 1, wherein the processor is to: analyze secondneuro-response data gathered from the first subject while exposed to asecond advertisement or entertainment; update the first response profilebased on the analysis of the second neuro-response data; and update theintegrated response profile based on the updated first response profileto form an updated integrated response profile.
 9. The system of claim1, wherein the processor is to aggregate the first response profile, thesecond response profile, and advertisement or entertainment selectionsincluding the first advertisement or entertainment over time to form theintegrated response profile.
 10. The method of claim 1, furtherincluding determining a duration of exposure to the first advertisementor entertainment based on the first difference.
 11. The method of claim1, further including determining a number of exposures to the firstadvertisement or entertainment based on the first difference.
 12. Atangible machine readable storage device or storage disc comprisinginstructions that, when executed, cause a machine to at least: determinea first distance between (1) a first peak in a first frequency band offirst neuro-response data gathered from a first subject while exposed tomedia and (2) a second peak in the first frequency band; determine asecond distance between (1) a third peak in the first frequency band andeither (2) the second peak in the first frequency band or (3) a fourthpeak in the first frequency band; determine a first difference betweenthe first distance and the second distance; generate a first responseprofile for the first subject to the media based on the firstdifference; integrate the first response profile with a second responseprofile associated with a second subject exposed to the media to form anintegrated response profile for the media; select a first advertisementor entertainment for presentation based on the integrated responseprofile; and modify the media to present the first advertisement orentertainment.
 13. The storage device or storage disk of claim 12,wherein the first response profile and the second response profile sharea characteristic indicative of a response of the first subject to themedia and the second subject to the media.
 14. The storage device orstorage disk of claim 12, wherein the first difference indicates a levelof engagement.
 15. The storage device or storage disk of claim 12,wherein the instructions, when executed, further cause the machine to:analyze second neuro-response data gathered from the first subject whileexposed to the first advertisement or entertainment; update the firstresponse profile based on the analysis of the second neuro-responsedata; and update the integrated response profile based on the updatedfirst response profile.
 16. The storage device or storage disk of claim12, wherein the instructions, when executed, further cause the machineto: analyze second neuro-response data gathered from the first subjectwhile exposed to a second advertisement or entertainment; update thefirst response profile based on the analysis of the secondneuro-response data; and update the integrated response profile based onthe updated first response profile.
 17. The storage device or storagedisk of claim 12, wherein the instructions, when executed, further causethe machine to aggregate the first response profile, the second responseprofile, and advertisement or entertainment selections including thefirst advertisement or entertainment over time to form the integratedresponse profile.
 18. The storage device or storage disk of claim 12,wherein the instructions, when executed, further cause the machine todetermine a duration of exposure to the first advertisement orentertainment based on the first difference.
 19. The storage device orstorage disk of claim 12, wherein the instructions, when executed,further cause the machine to determine a number of exposures to thefirst advertisement or entertainment based on the first difference. 20.A method for transforming a media presentation based on neuro-responsedata, the method comprising: determining, by executing an instructionwith a processor, a first distance between (1) a first peak in a firstfrequency band of first neuro-response data gathered from a firstsubject while exposed to media and (2) a second peak in the firstfrequency band; determining, by executing an instruction with theprocessor, a second distance between (1) a third peak in the firstfrequency band and either (2) the second peak in the first frequencyband or (3) a fourth peak in the first frequency band; determining, byexecuting an instruction with the processor, a first difference betweenthe first distance and the second distance; generating, by executing aninstruction with the processor, a first response profile for the firstsubject to the media based on the first difference; integrating, byexecuting an instruction with the processor, the first response profilewith a second response profile associated with a second subject exposedto the media to form an integrated response profile for the media;selecting, by executing an instruction with the processor, a firstadvertisement or entertainment for presentation based on the integratedresponse profile; and modifying, by executing an instruction with theprocessor, the media to present the first advertisement orentertainment.